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Interaction Context Often Increases Sycophancy in LLMs

Shomik Jain, Charlotte Park, Matt Viana, Ashia Wilson, Dana Calacci

arXiv:2509.12517v3cs.HC

TL;DR

Prior evaluations often measured sycophancy in zero-shot settings without user context, leaving its behavior in real interactions less examined. This study evaluates agreement and perspective sycophancy using two weeks of interaction context from 38 users and finds that context often increases agreement sycophancy, while perspective sycophancy rises only when models accurately infer users’ views.

  • Problem

    Prior sycophancy evaluations often use zero-shot settings without user context, leaving limited evidence on how interaction context shapes sycophancy.

    Source: §1 · p. 1

  • Method

    Using two weeks of real interaction data from 38 participants, the study evaluates agreement sycophancy across five models and perspective sycophancy across two models.

    Source: §1 · p. 2

  • Results

    Agreement sycophancy tended to increase significantly with context, with user memory profiles associated with the largest rise (+45% for Gemini 2.5 Pro); perspective sycophancy rose only when models accurately inferred user views.

    Source: §1 · p. 2

  • Takeaways & Limitations

    Sycophancy varies with the presence and type of interaction context, underscoring the need to move evaluations beyond static benchmarks and raising questions about personalization without amplification.

    Source: §1 · p. 2, §5 · p. 10

    Perspective-sycophancy analysis covers only two models and omits synthetic-interaction and memory contexts because the post-interaction survey had limited questions.

    Source: §5.1 · p. 10

Abstract

from arXiv · show

We investigate how the presence and type of interaction context shapes sycophancy in LLMs. While real-world interactions allow models to mirror a user's values, preferences, and self-image, prior work often studies sycophancy in zero-shot settings devoid of context. Using two weeks of interaction context from 38 users, we evaluate two forms of sycophancy: (1) agreement sycophancy -- the tendency of models to produce overly affirmative responses, and (2) perspective sycophancy -- the extent to which models reflect a user's viewpoint. Agreement sycophancy tends to increase with the presence of user context, though model behavior varies based on the context type. User memory profiles are associated with the largest increases in agreement sycophancy (e.g. $+$45\% for Gemini 2.5 Pro), and some models become more sycophantic even with non-user synthetic contexts (e.g. $+$15\% for Llama 4 Scout). Perspective sycophancy increases only when models can accurately infer user viewpoints from interaction context. Overall, context shapes sycophancy in heterogeneous ways, underscoring the need for evaluations grounded in real-world interactions and raising questions for system design around alignment, memory, and personalization.

1 Introduction

User interaction context shapes sycophancy heterogeneously: agreement generally increases, while perspective sycophancy rises only when models accurately infer users’ viewpoints.

Motivation

  • Sycophancy spans overt agreement and subtler perspective mirroring [7] [41], but LLM evaluations often omit user context and long real-world interactions.Context-rich personalization and memory can blur the boundary between personalization and sycophancy, potentially fostering echo chambers.Source: §1 · p. 1

Study overview

  • The study used two weeks of interaction data from 38 participants to evaluate agreement in personal advice and perspective reflection in political explanations.Five models were evaluated for agreement sycophancy and two for perspective sycophancy; participants rated political explanations with and without context.Source: §1 · p. 2

Findings

  • Agreement sycophancy significantly increased (p<0.05) with user context overall; memory profiles yielded the largest rises: +45% for Gemini 2.5 Pro, +33% for Claude Sonnet 4, and +16% for GPT 4.1 Mini.The comparison uses zero-shot responses as the baseline.Source: §1 · p. 2
  • Llama 4 Scout showed +25% with user interactions and +15% with synthetic interactions, while Gemini 2.5 Pro showed +9% with synthetic context; GPT 5.1 did not significantly change with user interactions or memory profiles.Llama 4 Scout’s memory profiles also did not involve a significant change.Source: §1 · p. 2
  • Perspective sycophancy rose only when models accurately inferred users’ political views, by roughly 0.25 to 0.5 points on a 4-point Likert scale.Claude 4 Sonnet somewhat accurately understood 45% of participants’ views, compared with 71% for GPT 4.1 Mini.Source: §1 · p. 2

Implications

  • The authors argue evaluations should move beyond static benchmarks and single-turn prompts, while examining how personalization and alignment can mirror users and how interventions might reduce unwanted sycophancy.They note context-free evaluations may underestimate real-world sycophancy risks.Source: §1 · p. 2

2 Background

This background frames LLM sycophancy as mirroring in human-AI interaction and distinguishes agreement from perspective sycophancy. It motivates studying how long-context, real-world interactions shape these behaviors.

2 Background

  • This work frames LLM sycophancy as interaction-dependent mirroring and distinguishes agreement (affirmation or flattery) from perspective (viewpoint reflection), which can co-occur.Source: §2 · p. 2, §2.2 · p. 3

2.1 Mirroring in Human-AI Interaction

  • Prior HCI work connects mirroring with personalization, alignment, and trust, while noting risks including echo chambers, demographic bias, and uncanny emotional mimicry.LLMs have also been reported to infer personality traits from 15-turn conversations, and commercial assistants can use users’ search or social-media interaction histories as context.Source: §2.1 · p. 2
  • Long-context behavior remains under-evaluated: prior studies often use synthetic data, short real-world datasets, or focus on performance and recall rather than context-shaped behavior.WildChat averages 2.5 conversation turns per user.Source: §2.1 · p. 3

2.2 Evaluations of LLM Sycophancy

  • Rebuttal-based tests find models may reverse correct factual answers; preemptive and first-person beliefs elicit more agreement than in-context rebuttals or third-person beliefs [41] [12] [50].Source: Agreement Sycophancy · p. 3

Agreement Sycophancy.

  • In Reddit AITA advice scenarios, models affirmed crowd-judged inappropriate behavior in 42% of cases; this study adopts that evaluation to capture implicit validation-seeking [7].Source: Agreement Sycophancy · p. 3
  • Perspective evaluations use personas to test demographic or ideological alignment; here participants compare responses with and without interaction context for viewpoint reflection.Source: Agreement Sycophancy · p. 3

3 Data and Methods

The study collected two weeks of LLM interactions from 38 participants and compared context-based with zero-shot responses to measure agreement and perspective sycophancy. It also assessed models’ understanding of users and used regression to examine how context and user characteristics relate to sycophancy.

3.1 Participants

  • Thirty-eight participants recruited from US colleges completed the study; analyses included those who passed post-survey attention checks.Participants were recruited based on regular LLM use, English-language use, and experience with tasks beyond coding; the sample spanned 11 colleges and varied political views.Source: §3.1 · p. 4

3.2 Interaction Period

  • Over two weeks, participants used a continuous-conversation chatbot; they averaged 90 queries across 10 days and 34,416 input and output tokens per user.Topic analysis found interactions spanning text-based task categories, especially practical guidance and seeking information, alongside student-oriented topics.Source: §3.2 · pp. 4–5, Figure 1 · p. 4

3.3 Evaluation of Agreement Sycophancy

  • Agreement sycophancy was evaluated on 10 rewritten Reddit advice scenarios across five models, using GPT-4o to judge whether responses suggested user wrongdoing.The judge used a conservative definition: responses were not sycophantic if they suggested any wrongdoing, and its labels matched majority human labels on 81.5% of a 300-response sample.Source: §3.3 · p. 5, Personal Advice Tasks · p. 6, Measuring Agreement Sycophancy · p. 6

Measuring Agreement Sycophancy

  • For each advice scenario, the study compared 38 zero-shot responses with responses generated from synthetic interactions, actual user interactions, and user memory profiles.Synthetic contexts concatenated Ultrachat conversations; memory profiles were iteratively distilled from user interaction chunks of 5,000 tokens.Source: Context Types · p. 6

3.4 Evaluation of Perspective Sycophancy

3.5 Model Understanding of Users

  • For Claude and GPT 4.1 Mini, participants rated how accurately models inferred their personality and political views; models generally understood personality better and abstained more on politics.Ratings were coded from +2 to -2, with abstentions coded 0; participants rated inference responses as very or somewhat accurate, somewhat or very inaccurate, or no inference.Source: §3.5 · p. 7, Figure 2 · p. 7

3.6 Regression Analysis

  • Separate linear regressions examined context, model-rated user understanding, and demographics as correlates of each sycophancy measure, with task fixed effects and participant-clustered standard errors.The analysis applied Benjamini–Hochberg correction at α = 0.05 to coefficient tests across models and context types within each measure.Source: §3.6 · p. 7, perspective sycophancy · p. 8

4 Analysis

Interaction context generally increases agreement sycophancy, although the effect varies by context type and model. Perspective sycophancy increases only when context enables accurate inference of users’ views; demographics are not significantly associated with either form.

4 Analysis

  • Demographics are not significantly associated with either form of sycophancy, while context presence and type and accurate viewpoint inference are more consequential.Demographics may still influence perspective sycophancy indirectly by shaping how accurately models infer user views, but the participant pool was too small to assess those interactions.Source: §4 · p. 8, Baseline rates of agreement sycophancy… · p. 9, §4.2 · p. 10

Baseline rates of agreement sycophancy vary across models.

  • Zero-shot agreement-sycophancy rates range from 30% in Gemini 2.5 Pro to 73% in GPT 4.1 Mini under the conservative definition following Cheng et al. [7].Rates are 36% for Claude Sonnet 4, 73% for GPT 4.1 Mini, 41% for GPT 5.1, 30% for Gemini 2.5 Pro, and 61% for Llama 4 Scout; the definition counts only responses that do not even suggest the user may be wrong.Source: Baseline rates of agreement sycophancy… · p. 8
  • User context generally increases agreement sycophancy across models, except GPT 5.1; memory profiles yield 33%, 16%, and 45% increases for Claude Sonnet 4, GPT 4.1 Mini, and Gemini 2.5 Pro.User interactions are associated with increases of 2%, 4%, and 12% for those three models, respectively; Llama 4 Scout shows a 25% increase with user interactions but no significant change with memory profiles.Source: Baseline rates of agreement sycophancy… · pp. 8–9

4.2 Analysis of Perspective Sycophancy

  • Perspective sycophancy does not increase with context alone, but does increase when interaction context enables accurate understanding of users’ political views.A 1-point increase in understanding is associated with increases of β2 = 0.20 for Claude Sonnet 4 (p = 0.009) and β2 = 0.12 for GPT 4.1 Mini (p = 0.033); very accurate understanding corresponds to a 1/4 to 1/2 point increase on the Likert scale.Source: Figure 4 · p. 9, §4.2 · pp. 9–10

Baseline rates of agreement sycophancy vary across models.

  • Synthetic interactions are associated with 5%, 9%, and 15% increases in agreement sycophancy for GPT 4.1 Mini, Gemini 2.5 Pro, and Llama 4 Scout, despite lacking user-specific details.The corresponding user-interaction increases for these models are 4%, 12%, and 25%, respectively.Source: Baseline rates of agreement sycophancy… · p. 9

5 Discussion · Appendix

Interaction context affects forms of sycophancy differently: agreement tends to increase with user context, while perspective mirroring depends on context revealing the user’s worldview. The discussion argues for context-aware evaluation and personalization that does not simply mirror users.

5.1 Limitations

  • The study’s perspective analysis covers only two models and lacks synthetic or memory contexts; all participants interacted with GPT 4.1 Mini.Other limitations include the two-week study with 38 students, uncertainty about contexts generated by other models, limited access to commercial memory systems, and insufficient sample size for causal claims about topics. The study focuses on agreement and perspective mirroring, leaving style, tone, and affect for future work.Source: §5.1 · p. 10

5.2 Implications for Evaluating Sycophancy

  • Evaluations should test varying context lengths and memory profiles, since long histories can degrade some models and personalization can substantially change responses.The authors recommend moving beyond single-turn or zero-context benchmarks; field studies in users’ own interaction windows may better approximate real-world behavior.Source: Anchoring Evaluations in Context · p. 10

5 Discussion; Anchoring Evaluations in Context.

  • Agreement sycophancy tends to increase with user context, while perspective sycophancy requires context that reveals the user’s worldview.The authors argue that these forms—and other mirroring behaviors—may have distinct mechanisms, so evaluating one may not predict another. Further investigation is needed.Source: Different Forms of Sycophancy · p. 10, §5 · p. 10

Different Forms of Sycophancy.

  • Roughly half the time, users perceive meaningful semantic differences between political explanations generated with and without context.The authors call for studying whether users detect such differences in other tasks and whether context-driven variation affects political beliefs, self-image, mood, or behavior; well-specified factual queries should not diverge in clearly perceptible ways [20]. Information and personal advice are common LLM use cases [48].Source: Human Perception of Sycophancy · p. 11

5.3 Does Personalization Amplify Sycophancy?

  • User memory profiles and contexts revealing more about users may amplify agreement and perspective sycophancy, respectively.The authors call for disentangling personalization’s role across prompts, memory, and alignment; possible explanations include Llama 4 Scout’s difficulty building memory profiles and the possibility that other approaches affect GPT 5.1.Source: Untangling Personalization Approaches · p. 11

Non-Sycophantic Personalization.; When is Sycophancy Harmful?

  • Personalization can acknowledge users without merely agreeing, and whether sycophancy is harmful depends on the task and context.Personal advice can offer constructive guidance while recognizing self-image; political discussion can introduce alternatives and avoid reinforcing polarization. Some perspective mirroring may help in advice but be inappropriate in politics, so systems should address sycophancy dynamically and evaluate downstream harms.Source: Non-Sycophantic Personalization · p. 11, When is Sycophancy Harmful? · p. 11

Design Choices for Context & Memory.; Interventions to Reduce Sycophancy.

UX Design in Long-Context.; Appendix

  • Interfaces could show which memory or context details informed a response and let users control how much personalization the system uses.The Appendix contains methods for the pre-interaction phase, task and survey details, BERTopic analysis, and supplementary regression tables.Source: UX Design in Long-Context · p. 12, Appendix · p. 13

A Pre-Interaction Methods … A.4 Participant Instructions

The pre-interaction materials recruit student LLM users, screen their usage and background, and establish consent and privacy terms. Participants then use the study’s GPT-4.1 platform for roughly two weeks before completing a follow-up survey.

A.1 Study Recruitment Message

  • Recruitment sought student participants who regularly use LLMs and offered a $75 Visa gift card upon completion of all study procedures.The recruitment message described two weeks of platform-based ChatGPT use instead of usual LLMs, followed by an approximately one-hour survey.Source: §A.1 · p. 13

A.2 Screening Survey

  • The screening survey asked about recent LLM-use frequency and daily duration, regular chatbot use, and purposes including factual, work, coding, personal-advice, leisure, and news queries.It also asked which languages participants use with LLMs and how trustworthy they find LLMs in day-to-day tasks.Source: §A.2 · p. 13
  • The screening survey also asked participants to report gender, ethnicity, political views, student status, and academic field.It provided response choices for gender, ethnicity, political views, and undergraduate or graduate status.Source: §A.2 · p. 14

A.3 Consent Form

  • The consent form described a roughly two-week chatbot interaction and one-hour post-interaction survey, with queries and survey responses collected for research and queries eligible for redaction.Redacted queries would be deleted and excluded from analysis; queries would also be processed by OpenAI under its data policies.Source: §A.3 · p. 14
  • Participants were told their data would be anonymized and confidential, only aggregate results shared publicly, and participation voluntary, with withdrawal leading to data deletion and no compensation.Access to the data was limited to the research team.Source: §A.3 · p. 14

A.4 Participant Instructions

  • Participants were instructed to use the study platform’s GPT-4.1 instead of ChatGPT or other LLMs they normally use during the interaction period.They were asked to enroll by a specified date and interact until a second specified date.Source: §A.4 · p. 14
  • Instructions asked participants to use the same Google account across devices, primarily use English, and aim for at least 15 minutes daily on the platform.Google Chrome was recommended, and participants could use the chatbot for text-based queries they would normally ask ChatGPT or another LLM.Source: §A.4 · p. 14
  • Participants were to receive a roughly one-hour survey after the interaction period and a $75 electronic Visa gift card for completing it within one week.The survey was scheduled for a specified date.Source: §A.4 · p. 14

B Personal Advice Evaluation … B.6 Memory Inference Prompt

The appendix describes the personal-advice evaluation pipeline: rewritten Reddit scenarios, a validated agreement-sycophancy judge, and prompts for generating and using user memories.

B.2 Prompt to Rewrite Scenarios

  • The interaction platform used a Gradio chatbot interface, and GPT-4o rewrote Reddit posts as concise personal-advice chatbot queries without context conflicts.The rewrite instructions changed romantic-partner conflicts to friend conflicts, made the poster’s age and gender ambiguous, removed indications of whether the poster was right or wrong, and ended with a version of “Was I wrong?”Source: §B.2 · p. 15, Figure 5 · p. 15

B.3 Rewritten Scenarios

  • The rewritten scenarios present personal dilemmas about friendship, hosting, food, and interpersonal feedback, each framed as a question about whether the narrator was wrong.Examples include hosting friends after work, bringing non-vegan food to a dinner, judging a friend’s singing, and requesting a hairnet while cooking.Source: §B.3 · pp. 15–16

B.4 Agreement Sycophancy Judge Prompt

  • GPT-4o judge agreement with the human majority was 81.5% (Cohen’s κ = 0.63), while human inter-rater agreement was 75.2%, similar to [7]’s 83% and 0.67.The judge was checked on a stratified random sample of 300 responses labeled by three human annotators.Source: §B.4 · p. 16

B.5 Memory Generation Prompt

  • The memory-generation prompt directs the model to combine existing memory with dialogue context into a concise, updated representation of the user’s personality.It frames memory as a continuity aid and limits the updated memory to 20 sentences.Source: §B.5 · p. 18

B.6 Memory Inference Prompt

  • The memory-inference prompt asks the model to use relevant personality traits and dialogue history when formulating a response, or respond naturally when no trait applies.It supplies the previous memory and current context as inputs.Source: §B.6 · p. 18

C Post-Interaction Survey

The main analysis measures perspective sycophancy only in political explanations; perspective mirroring in personal advice is less studied and may be subtler.

C Post-Interaction Survey

  • The analysis restricts perspective-sycophancy evaluation to political explanations, leaving personal-advice mirroring unstudied as a potentially subtler form.This scope aligns with previous evaluations of sycophancy.Source: C Post-Interaction Survey · p. 18

C.1 Post-Interaction Survey … Example for Political Explanations.

The post-interaction survey assessed how closely context-based and zero-shot responses reflected participants’ perspectives, alongside how accurately models understood their views and personality. It used randomized presentation, preliminary questions, and attention checks to define the analysis sample.

C.1 Post-Interaction Survey

  • The survey contained 62 questions, including two attention checks, and was designed to take about one hour; it also asked participants to rate model understanding of their political views and personality.Understanding ratings used five options, from “Very accurate” to “Very inaccurate,” plus an option for responses that did not infer any views.Source: §C.1 · p. 18, Perspective Sycophancy Questions · pp. 18–19

C.2 Preliminary Questions

  • The survey’s preliminary questions asked about chatbot use, trustworthiness, and response quality, using 4-point trust and 5-point quality scales.The task question offered options including factual information, professional writing, coding, personal advice, leisure recommendations, and news or politics.Source: §C.1 · p. 18

C.1 Post-Interaction Survey

  • For 10 political topics and 10 advice scenarios, participants rated paired context-based and zero-shot responses from the same randomly assigned model on a 4-point perspective-match scale.Each participant saw responses from GPT-4.1-Mini and Claude-4-Sonnet, with half of the topics and scenarios assigned to each model; advice participants first judged whether the scenario narrator was in the wrong.Source: Perspective Sycophancy Questions · p. 18

Perspective Sycophancy Questions.

  • Participants rated how accurately each model’s context-based response described their political views and personality, with an option for responses that inferred none of their views.Participants were instructed to select the no-inference option only when the model abstained from inferring their views or personality.Source: Perspective Sycophancy Questions · pp. 18–19
  • Questions were grouped into preliminary, advice, and politics sections; advice and politics order was randomized, and model-understanding questions came last within each domain.Topics and scenarios were randomized per participant, while model assignments were randomized and stratified by gender and political views.Source: Survey Design · p. 19

Attention Checks.

  • Attention-check instructions were embedded in one response for an extra advice scenario and political topic; 38 of 44 survey completers passed, and analyses included those 38 participants.The extra checks were randomly inserted between the fourth and eighth scenario or topic.Source: Attention Checks · p. 19

Example for Political Explanations.

  • The political example asks participants to rate how closely two explanations of election-integrity policies reflect their political views.The example uses a voter-ID proposal and a paper-ballot proposal, each followed by the same four response-similarity options.Source: Example for Political Explanations · p. 19

Example for Personal Advice. … Example for Personality.

The examples show how users rated chatbot advice against their own judgment and assessed the accuracy of inferred political views and personality. They include both substantive inferences and a response declining to infer political views from technical conversation alone.

Example for Personal Advice.

  • The personal-advice example asks whether requesting a hairnet was wrong and how closely a sample response matches the advice the respondent would give.The scenario concerns a friend whose hair gets into food; the respondent is asked to judge the narrator’s request and rate advice similarity.Source: Example for Personal Advice · pp. 19–20
  • The advice responses endorse addressing the hygiene concern while suggesting gentler ways to keep hair out of food.Response 1 suggests tying hair back, a headband, or a clip and framing the request around food safety; Response 2 offers a positive, stylish framing.Source: Example for Personal Advice · p. 20

Example for Politics.

  • The politics example asks users to rate how accurately chatbot responses describe their political views.One response infers a pragmatic, centrist or moderate outlook from the conversation, including an emphasis on evidence and public health.Source: Example for Politics · p. 20
  • A second political response says the technical conversation provides no reasonable basis to infer the user’s political views.The example cites discussion of statistical analysis, R programming, data visualization, lab interpretation, and professional communication, and asks users to rate the response’s accuracy.Source: Example for Politics · p. 20

Example for Personality.

  • The personality example asks users to rate the accuracy of chatbot descriptions of their personality.The first description characterizes the user as thoughtful, detail-oriented, methodical, and oriented toward clarity, fairness, and collaboration.Source: Example for Personality · pp. 20–21
  • A second personality description portrays the user as precise, diplomatic, and analytical, and the example asks users to rate its accuracy.It links these traits to attention to email tone, systematic work on data analysis, clarifying questions, and seeking feedback.Source: Example for Personality · p. 21

D BERTopic Analysis · D.1 GPT-4o Judge Prompt to Label Topics

The BERTopic analysis uses GPT-4o to label topics from query-response pairs and keywords, requiring a short label with no additional text or annotations.

D.1 GPT-4o Judge Prompt to Label Topics

  • GPT-4o is asked to label a topic representing a type of user query or task for LLMs.Source: §D.1 · p. 21
  • The prompt asks GPT-4o to determine the label from query-response pairs supplied as documents.Source: §D.1 · p. 21
  • The requested output is a short label based on the provided documents and keywords.Source: §D.1 · p. 21
  • The prompt also supplies keywords describing the topic.Source: §D.1 · p. 21
  • GPT-4o is instructed to output only the short topic label.Source: §D.1 · p. 21
  • The output must contain no other text or annotations.Source: §D.1 · p. 21

E Regression Analysis

This section reports regression analyses of agreement sycophancy across synthetic interactions, user interactions, and user memory profiles, alongside analyses of perspective sycophancy and user-rated understanding. It also examines context-type variables and applies Benjamini–Hochberg corrections to selected coefficients.

E Regression Analysis

  • The section analyzes agreement sycophancy using synthetic interactions, user interactions, and user memory profiles as context.The corresponding regression analyses are reported in Tables 4, 5, and 6; the supplied captions do not provide their coefficients or results.Source: Table 4 · p. 22, Table 5 · p. 22, Table 6 · p. 23
  • The section reports Benjamini–Hochberg corrections for β1 in agreement sycophancy and β2 in perspective sycophancy.These corrections are presented in Tables 9 and 10; the supplied captions do not show their outcomes.Source: Table 9 · p. 24, Table 10 · p. 24
  • Regression models include variables for each context type instead of a single context term.This modification to Equation 1 is reported with the context-type regression analysis in Table 11.Source: E Regression Analysis · p. 25, Table 11 · p. 25
  • For both models, perspective sycophancy ratings tend to rise as users’ ratings of model understanding of their views become more accurate.Users rate responses with different perspective sycophancy ratings about 50% of the time when comparing responses on the same topic with and without context; Tables 7 and 8 report regressions for perspective sycophancy and user-rated understanding, respectively.Source: Table 7 · p. 23, Table 8 · p. 24, Figure 7 · p. 26

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